A New Method for Ligand-supported Homology Modelling of Protein Binding Sites: Development and Application to the neurokinin-1 receptor

In this thesis, a novel strategy (MOBILE (Modelling Binding Sites Including Ligand Information Explicitly)) was developed that models protein binding-sites simultaneously considering information about the binding mode of bioactive ligands during the homology modelling process. As a result, protein binding-site models of higher accuracy and relevance can be generated. Starting with the (crystal) structure of one or more template proteins, in the first step several preliminary homology models of the target protein are generated using the homology modelling program MODELLER. Ligands are then placed into these preliminary models using different strategies depending on the amount of experimental information about the binding mode of the ligands. (1.) If a ligand is known to bind to the target protein and the crystal structure of the protein-ligand complex with the related template protein is available, it can be assumed that the ligand binding modes are similar in the target and template protein. Accordingly, ligands are then transferred among these structures keeping their orientation as a restraint for the subsequent modelling process. (2.) If no complex crystal structure with the template is available, the ligand(s) can be placed into the template protein structure by docking, and the resulting orientation can then be used to restrain the following protein modelling process. Alternatively, (3.) in cases where knowledge about the binding mode cannot be inferred by the template protein, ligand docking is performed into an ensemble of homology models. The ligands are placed into a crude binding-site representation via docking into averaged property fields derived from knowledge-based potentials. Once the ligands are placed, a new set of homology models is generated. However, in this step, ligand information is considered as additional restraint in terms of the knowledge-based DrugScore protein-ligand atom pair potentials. Consulting a large ensemble of produced models exhibiting di erent side-chain rotamers for the binding-site residues, a composite picture is assembled considering the individually best scored rotamers with respect to the ligand. After a local force-field optimisation, the obtained binding-site models can be used for structure-based drug design

Enhancing the fight against malaria : from genome to structure and activity of a G-protein coupled receptor from the mosquito, Anopheles Gambiae

Includes abstract.Includes bibliographical references (leaves 183-184).G-proton coupled receptors (GPCRs) are excellent drug targets that occupy a central position in the physiology of insects and are involved in transmission of signal from the extracellular to the intracellular side of the cell. Adipokinetic hormone receptors (AKHRs) are GPCRs that mediate physiological functions of the neurohormones, adipokinetic hormones (AKHs) that regulate mobilisation of energy reserves during mosquito flight. Ligand binding to GPCRs depends on the three dimensional (3D) structures of the receptors but to date no crystal structures of insect GPCRs are available. This work focused on building molecular models of AKHR from the genome of the malaria mosquito, identifying its binding site and studying the conformational and structural changes during molecular dynamics of the active and inactive receptor

Biochemistry of opioid (morphine) receptors : binding, structure and molecular modelling

Morphine is the most widely used compound among narcotic analgesics and remains the gold standard when the effects of other analgetic drugs are compared. The most characteristic effect of morphine is the modulation of pain perception resulting in an increase in the threshold of noxious stimuli. Antinociception induced by morphine is mediated via opioid receptors, namely the μ-type opioid receptor. Apart from the μ-opioid receptor, two other classical opioid receptors κ- and δ- and one non-classical opioid receptor, the nociceptin receptor was discovered and cloned so far. At the same time endogenous opioids were also discovered, such as enkephalins, endorphins, and dynorphins. The opioid receptors together with the endogenous opioids form the so called endogenous opioid system, which is highly distributed throughout the body and apart from analgesia it has several other important physiological functions. In this article we will review the historical milestones of opioid research − in detail with morphine. The review will also cover the upmost knowledge in the molecular structure and physiological effects of opioid receptors and endogenous opioids and we will discuss opioid receptor modelling − a rapidly evolving field in opioid receptor research

Prediction of the Binding Affinity between Fenoterol Derivatives and the β2-Adrenergic Receptor Using Atom-Based 3D-Chiral Linear Indices

The non-stochastic and stochastic atom-based 3D-chiral quadratic indices were applied to the study of the β2-adrenoceptor (β2-AR) agonist effect (binding affinities) between a set of 26 stereoisomers of fenoterol, reported with this activity. Linear multiple regression analysis was carried out to predict the β2-AR binding affinities of the stereoisomers. Two statistically significant QSAR models, able to describe more than the 92% of the variance of the experimental binding affinities, were obtained using non-stochastic (R2 = 0.924 and s = 0.21) and stochastic (R2 = 0.92 and s = 0.22) 3D-chiral linear indices, respectively. The predictability and stability (robustness) of the obtained models (assessed by the leave-one-out cross-validation experiment) yielded values of q2 = 0.893 (scv = 0.237) and q2 = 0.886 (scv = 0.245), respectively. The results obtained with our approach were slightly better than the results of a 3D-QSAR model, obtained with the CoMFA method (R2 = 0.920, q2 = 0.847 and scv = 0.309). The results of our work demonstrate the usefulness of our topological approach for drug discovery of new lead compounds, even in those studies in which the three-dimensional configuration of the chemicals play an important role in the biological activity.Los índices lineales 3D-quirales no-estocásticos y estocásticos basados en relaciones de átomos son aplicados al estudio del efecto agonista (afinidad de unión) sobre el receptor adrenérgico β2 (β2-AR) entre una serie de 26 estereoisómeros del fenoterol, a los cuales se les ha reportado esta actividad. Una regresión lineal múltiple es llevada a cabo para predecir la afinidad de unión β2-AR de los estereoisómeros. Se obtienen dos modelos QSAR estadísticamente significativos, capaces de describir más del 92 % de la varianza experimental de las afinidades de unión, empleando los índices lineales 3D-quirales no-estocásticos (R2 = 0.924 y s = 0.21) y estocásticos (R2 = 0.92 y s = 0.22) respectivamente. El poder predictivo y la robustez de los modelos obtenidos (comprobados mediante una validación cruzada dejando-uno-fuera) alcanzan valores de q2 = 0.893 (scv = 0.237) y q2 = 0.886 (scv = 0.245), correspondientemente. Los resultados obtenidos con nuestro enfoque fueron ligeramente superiores a aquellos resultados obtenidos previamente con un modelo 3D-QSAR, empleando el método CoMFA (R2 = 0.920, q2 = 0.847 y scv = 0.309). Los resultados de nuestro trabajo demuestran la utilidad de nuestro enfoque topológico para el descubrimiento de nuevos compuestos líderes candidatos a fármacos, incluso para estudios en los cuales las conformaciones tridimensionales de los compuestos juegan un rol fundamental en la actividad biológica.Ciencias Experimentale

In Silico Veritas: The Pitfalls and Challenges of Predicting

Recently the first community-wide assessments of the prediction of the structures of complexes between proteins and small molecule ligands have been reported in the so-called GPCR Dock 2008 and 2010 assessments. In the current review we discuss the different steps along the protein-ligand modeling workflow by critically analyzing the modeling strategies we used to predict the structures of protein-ligand complexes we submitted to the recent GPCR Dock 2010 challenge. These representative test cases, focusing on the pharmaceutically relevant G Protein-Coupled Receptors, are used to demonstrate the strengths and challenges of the different modeling methods. Our analysis indicates that the proper performance of the sequence alignment, introduction of structural adjustments guided by experimental data, and the usage of experimental data to identify protein-ligand interactions are critical steps in the protein-ligand modeling protocol. © 2011 by the authors; licensee MDPI, Basel, Switzerland